Senior Product Manager - Document Verification
SocureRemote - USPosted 26 March 2026
Tech Stack
Job Description
Senior Product Manager - Document Verification
WHY SOCURE?
Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.
ABOUT THE ROLE
We are looking for a Senior Product Manager to own critical components of Socure’s Document Verification (DocV) platform, with a focus on the forensic engine, decisioning logic, and computer vision models.
This role sits at the intersection of machine learning, fraud detection, and product decisioning, and is responsible for driving the systems that translate signals into outcomes. You will work closely with Data Science and Engineering to improve detection of fraud vectors such as injection attacks, deepfakes, and document manipulation, while also shaping how those signals are operationalized into scalable and configurable decisioning frameworks.
This is a highly technical and impact-driven role requiring strong product judgment, deep curiosity about fraud patterns, and the ability to translate complex model behavior into clear product logic and customer-facing outcomes.
WHAT YOU'LL DO
Forensic Engine & Detection Strategy
- Own the roadmap and execution for DocV’s forensic engine, including detection of document fraud, injection attacks, and AI-generated content.
- Support efforts to scale DocV adoption globally, including in the public sector, financial services, and emerging markets.
- Partner with Data Science to define, evaluate, and improve model performance across key fraud vectors.
- Identify gaps in detection coverage and drive new signal development across image, video, and device layers.
Decisioning & Risk Logic
- Design and evolve decisioning frameworks that translate model outputs into actionable outcomes.
- Build scalable, configurable logic that supports diverse customer risk profiles and use cases.
- Balance fraud detection performance with user experience and conversion impact.
Customer-Centric Product Development
- Work closely with high-value customers to understand fraud patterns, edge cases, and operational needs.
- Translate customer feedback into product improvements and prioritization decisions.
- Support complex customer implementations and act as a subject matter expert in DocV decisioning.
Cross-Functional Leadership
- Collaborate with Engineering and Data Science to translate product requirements into technical execution.
- Partner with the Fraud Investigation team, Customer Success, and Sales to align on product behavior and outcomes.
- Drive alignment on tradeoffs between detection accuracy, false positives, and business impact.
Data & Performance Analysis
- Use SQL and analytics tools to evaluate model performance, decisioning outcomes, and conversion impact.
- Define and track key metrics related to fraud detection, model precision/recall, and user experience.
- Conduct deep dives into fraud patterns and emerging attack vectors.
Go-To-Market & Enablement
- Support product launches and enhancements with clear positioning and documentation.
- Enable internal teams and customers to understand and effectively use decisioning capabilities.
WHAT YOU BRING
- Experience: 3–5 years in product management, preferably in identity verification, fraud prevention, or other ML-driven products.
- Technical Expertise: Strong understanding of APIs, SQL queries, databases, and product architecture.
- Machine Learning Familiarity: Experience working closely with ML models ... (truncated, view full listing at source)
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